Heretofore, learning technologies for when inputting characters have been variously proposed, as shown in JP 2004-536369T and JP H9-101949A.
With the conventional learning technologies, input characters and conversion characters that are based on the input characters are extracted. With the conventional learning technologies, input characters as readings and conversion characters as conversion candidates are associated with each other. The associated characters and conversion candidates are then stored in a conversion learning database. Thereafter, when a learned character is input, the conversion learning database is referred to, and a conversion candidate that corresponds to this character is read.
JP 2004-536369T and JP H9-101949A are examples of background art.
However, with the conventional learning technologies, input characters are not learned during character input, that is, when the input characters are not yet finalized. For example, in the case where a character is deleted during input, that is, in the case where misinput of a character arises before finalizing the input characters, the input characters are not learned. Accordingly, learning cannot be performed until the correct characters intended by the user are input.
In this case, when a similar misinput arises again, the user had to delete the character and input of the correct character again.
Accordingly, one or more embodiments may provide a character input technology that is able to register an appropriate conversion candidate, even when misinput arises.